Functionality

class LocalFeatureSelection(self, alpha=19, gamma=0.2, tau=2, sigma=1, n_beta=20, nrrp=2000, knn=1, rr_seed=None)

Parameters

alpha : integer, optional, default 19 maximum number of selected features for each representative sample gamma : integer, optional, default 0.2 impurity level tau : integer, optional, default 2 number of iterations sigma : integer, optional, default 1 controls neighboring samples weighting n_beta : integer, optional, default 20 number of distinct beta nrrp : integer, optional, default 2000 number of iterations for randomized wandering process knn : integer, optional, default 1 k nearest neighbours rr_seed : integer, optional, default None seed value for random wandering process

Attributes

fstar : array of shape (n_features, n_features) selected features for each sample fstar_lin : array of shape (n_features, n_features) fstar before applying randomized wandering process training_data : array of shape (n_features, n_samples The set of M by N features and observations the model was trained on training_labels : array of shape (n_samples) The set of N class labels the model was trained on

Methods

fit(self, training_data, training_labels)
predict(self, testing_data)
classification(self, testing_data)
class_sim_m(self, test, N, patterns, targets, fstar)

__init__(self, alpha=19, gamma=0.2, tau=2, sigma=1, n_beta=20, nrrp=2000, knn=1, rr_seed=None)

Initialize self

fit(self, training_data, training_labels)

Fit model

Parameters	training_data : {array-like} of shape (n_samples, m_features) Training data training_labels : {array-like} of shape (n_samples) Class labels for each sample
Returns

predict(self, testing_data)

Predict using the model

Parameters	testing_data : {array-like} of shape (n_samples, m_features) Testing data
Returns

classification(self, testing_data)

Internal feature classification function, called by predict function

Parameters	testing_data : {array-like} of shape (n_samples, m_features) Testing data
Returns

class_sim_m(self, test, N, patterns, targets, fstar, gamma, knn)

Internal feature classification function, called by classification function

Parameters	test : {array-like} of shape (n_samples, m_features) Testing data N: {integer} Number of features patterns: Data the model was trained on targets: Class Labels the model was trained on fstar: Selected features for each samples gamma: Impurity Level knn: K nearest neighbours
Returns